6.5840 - Spring 2026

6.5840 Lab 1: MapReduce

Collaboration policy // Submit lab // Setup Go // Guidance // Piazza

Introduction

In this lab you'll build a MapReduce system. You'll implement a worker process that calls application Map and Reduce functions and handles reading and writing files, and a coordinator process that hands out tasks to workers and copes with failed workers. You'll be building something similar to the MapReduce paper. (Note: this lab uses "coordinator" instead of the paper's "master".)

Getting started

You need to setup Go to do the labs.

Fetch the initial lab software with git (a version control system). To learn more about git, look at the Pro Git book or the git user's manual.

$ git clone git://g.csail.mit.edu/6.5840-golabs-2026 6.5840
$ cd 6.5840
$ ls
Makefile src
$

We supply you with a simple sequential mapreduce implementation in src/main/mrsequential.go. It runs the maps and reduces one at a time, in a single process. We also provide you with a couple of MapReduce applications: word-count in mrapps/wc.go, and a text indexer in mrapps/indexer.go. You can run word count sequentially as follows:

$ cd ~/6.5840
$ cd src/main
$ go build -buildmode=plugin ../mrapps/wc.go
$ rm mr-out*
$ go run mrsequential.go wc.so pg*.txt
$ sort mr-out-0
A 509
ABOUT 2
ACT 8
ACTRESS 1 
...

(You might need to set LC_COLLATE=C environment variable for sort to produce the above output: LC_COLLATE=C sort mr-out-0)

mrsequential.go leaves its output in the file mr-out-0. The input is from the text files named pg-xxx.txt.

Feel free to borrow code from mrsequential.go. You should also have a look at mrapps/wc.go to see what MapReduce application code looks like.

For this lab and all the others, we might issue updates to the code we provide you. To ensure that you can fetch those updates and easily merge them using git pull, it's best to leave the code we provide in the original files. You can add to the code we provide as directed in the lab write-ups; just don't move it. It's OK to put your own new functions in new files.

Your Job

Your job is to implement a distributed MapReduce, consisting of two programs, the coordinator and the worker. There will be just one coordinator process, and one or more worker processes executing in parallel. In a real system the workers would run on a bunch of different machines, but for this lab you'll run them all on a single machine. The workers will talk to the coordinator via RPC. Each worker process will, in a loop, ask the coordinator for a task, read the task's input from one or more files, execute the task, write the task's output to one or more files, and again ask the coordinator for a new task. The coordinator should notice if a worker hasn't completed its task in a reasonable amount of time (for this lab, use ten seconds), and give the same task to a different worker.

We have given you a little code to start you off. The "main" routines for the coordinator and worker are in main/mrcoordinator.go and main/mrworker.go; don't change these files. You should put your implementation in mr/coordinator.go, mr/worker.go, and mr/rpc.go.

Here's how to run your code on the word-count MapReduce application. First, build the word-count plugin:

$ cd main
$ go build -buildmode=plugin ../mrapps/wc.go

In one window, run the coordinator:

$ rm mr-out*
$ go run mrcoordinator.go sock123 pg-*.txt

The sock123 argument specifies a socket on which the coordinator receives RPCs from workers. The pg-*.txt arguments to mrcoordinator.go are the input files; each file corresponds to one "split", and is the input to one Map task.

In one or more other windows, run some workers:

$ go run mrworker.go wc.so sock123

When the workers and coordinator have finished, look at the output in mr-out-*. When you've completed the lab, the sorted union of the output files should match the sequential output, like this:

$ cat mr-out-* | sort | more
A 509
ABOUT 2
ACT 8
ACTRESS 1 
...

We supply you with all the tests that we'll use to grade your submitted lab. The source code for the tests are in mr/mr_test.go. You can run the tests in the src directory:

$ cd src
$ make mr
...

The tests check that the wc and indexer MapReduce applications produce the correct output when given the pg-xxx.txt files as input. The tests also check that your implementation runs the Map and Reduce tasks in parallel, and that your implementation recovers from workers that crash while running tasks.

If you run the tests now, they will hang in the first test:

$ cd ~/6.5840/src
$ make mr
...
cd mr; go test -v -race 
=== RUN   TestWc
...

You can change ret := false to true in the Done function in mr/coordinator.go so that the coordinator exits immediately. Then:

$ make mr
...
=== RUN   TestWc
2026/01/22 14:56:24 reduce created no mr-out-X output files!
exit status 1
FAIL    6.5840/mr       4.516s
make: *** [Makefile:44: mr] Error 1
$

The tests expect to see output in files named mr-out-X, one for each reduce task. The empty implementations of mr/coordinator.go and mr/worker.go don't produce those files (or do much of anything else), so the test fails.

When you've finished, the test output should look like this:

$ make mr
...
=== RUN   TestWc
--- PASS: TestWc (8.64s)
=== RUN   TestIndexer
--- PASS: TestIndexer (5.90s)
=== RUN   TestMapParallel
--- PASS: TestMapParallel (7.05s)
=== RUN   TestReduceParallel
--- PASS: TestReduceParallel (8.05s)
=== RUN   TestJobCount
--- PASS: TestJobCount (10.04s)
=== RUN   TestEarlyExit
--- PASS: TestEarlyExit (6.05s)
=== RUN   TestCrashWorker
2026/01/22 14:58:14 *re*-starting map ../../main/pg-tom_sawyer.txt 0
2026/01/22 14:58:14 *re*-starting map ../../main/pg-metamorphosis.txt 2
2026/01/22 14:58:39 *re*-starting map ../../main/pg-metamorphosis.txt 2
2026/01/22 14:58:40 map 2 already done
2026/01/22 14:58:45 *re*-starting reduce 0
--- PASS: TestCrashWorker (40.18s)
PASS
ok      6.5840/mr       86.932s
$

Depending on your strategy for terminating worker processes, you may see errors like:

2026/02/11 16:21:32 dialing:dial unix /var/tmp/5840-mr-501: connect: connection refused

It is fine to see a handful of these messages per test; they arise when the worker is unable to contact the coordinator RPC server after the coordinator has exited.

A few rules:

The map phase should divide the intermediate keys into buckets for nReduce reduce tasks, where nReduce is the number of reduce tasks -- the argument that main/mrcoordinator.go passes to MakeCoordinator(). Each mapper should create nReduce intermediate files for consumption by the reduce tasks.
The worker implementation should put the output of the X'th reduce task in the file mr-out-X.
A mr-out-X file should contain one line per Reduce function output. The line should be generated with the Go "%v %v" format, called with the key and value. Have a look in main/mrsequential.go for the line commented "this is the correct format". The tests will fail if your implementation deviates too much from this format.
You can modify mr/worker.go, mr/coordinator.go, and mr/rpc.go. You can temporarily modify other files for testing, but make sure your code works with the original versions; we'll test with the original versions.
The worker should put intermediate Map output in files in the current directory, where your worker can later read them as input to Reduce tasks.
main/mrcoordinator.go expects mr/coordinator.go to implement a Done() method that returns true when the MapReduce job is completely finished; at that point, mrcoordinator.go will exit.
When the job is completely finished, the worker processes should exit. A simple way to implement this is to use the return value from call(): if the worker fails to contact the coordinator, it can assume that the coordinator has exited because the job is done, so the worker can terminate too. Depending on your design, you might also find it helpful to have a "please exit" pseudo-task that the coordinator can give to workers.

Hints

The Guidance page has some tips on developing and debugging.
One way to get started is to modify mr/worker.go's Worker() to send an RPC to the coordinator asking for a task. Then modify the coordinator to respond with the file name of an as-yet-unstarted map task. Then modify the worker to read that file and call the application Map function, as in mrsequential.go.
The application Map and Reduce functions are loaded at run-time using the Go plugin package, from files whose names end in .so.
If you change anything in the mr/ directory, you will probably have to re-build any MapReduce plugins you use, with something like go build -buildmode=plugin ../mrapps/wc.go. make mr builds the plugins for you. You can run an individual test using make RUN="-run Wc" mr`, which passes "-run Wc" to go go test, and selects any test from mr/mr_test.go matching Wc.
This lab relies on the workers sharing a file system. That's straightforward when all workers run on the same machine, but would require a global filesystem like GFS if the workers ran on different machines.
A reasonable naming convention for intermediate files is mr-X-Y, where X is the Map task number, and Y is the reduce task number.
The worker's map task code will need a way to store intermediate key/value pairs in files in a way that can be correctly read back during reduce tasks. One possibility is to use Go's encoding/json package. To write key/value pairs in JSON format to an open file:
```
  enc := json.NewEncoder(file)
  for _, kv := ... {
    err := enc.Encode(&kv)
```
and to read such a file back:
```
  dec := json.NewDecoder(file)
  for {
    var kv KeyValue
    if err := dec.Decode(&kv); err != nil {
      break
    }
    kva = append(kva, kv)
  }
```
The map part of your worker can use the ihash(key) function (in worker.go) to pick the reduce task for a given key.
You can steal some code from mrsequential.go for reading Map input files, for sorting intermedate key/value pairs between the Map and Reduce, and for storing Reduce output in files.
The coordinator, as an RPC server, will be concurrent; don't forget to lock shared data.
Workers will sometimes need to wait, e.g. reduces can't start until the last map has finished. One possibility is for workers to periodically ask the coordinator for work, sleeping with time.Sleep() between each request. Another possibility is for the relevant RPC handler in the coordinator to have a loop that waits, either with time.Sleep() or sync.Cond. Go runs the handler for each RPC in its own thread, so the fact that one handler is waiting needn't prevent the coordinator from processing other RPCs.
The coordinator can't reliably distinguish between crashed workers, workers that are alive but have stalled for some reason, and workers that are executing but too slowly to be useful. The best you can do is have the coordinator wait for some amount of time, and then give up and re-issue the task to a different worker. For this lab, have the coordinator wait for ten seconds; after that the coordinator should assume the worker has died (of course, it might not have).
If you choose to implement Backup Tasks (Section 3.6), note that we test that your code doesn't schedule extraneous tasks when workers execute tasks without crashing. Backup tasks should only be scheduled after some relatively long period of time (e.g., 10s).
To test crash recovery, you can use the mrapps/crash.go application plugin. It randomly exits in the Map and Reduce functions.
To ensure that nobody observes partially written files in the presence of crashes, the MapReduce paper mentions the trick of using a temporary file and atomically renaming it once it is completely written. You can use ioutil.TempFile (or os.CreateTemp if you are running Go 1.17 or later) to create a temporary file and os.Rename to atomically rename it.
Go RPC sends only struct fields whose names start with capital letters. Sub-structures must also have capitalized field names.
When calling the RPC call() function, the reply struct should contain all default values. RPC calls should look like this:
```
  reply := SomeType{}
  call(..., &reply)
```
without setting any fields of reply before the call. If you pass reply structures that have non-default fields, the RPC system may silently return incorrect values.

No-credit challenge exercises

Implement your own MapReduce application (see examples in mrapps/*), e.g., Distributed Grep (Section 2.3 of the MapReduce paper).

Get your MapReduce coordinator and workers to run on separate machines, as they would in practice. You will need to set up your RPCs to communicate over TCP/IP instead of Unix sockets (see the commented out line in Coordinator.server()), and read/write files using a shared file system. For example, you can ssh into multiple Athena cluster machines at MIT, which use AFS to share files; or you could rent a couple AWS instances and use S3 for storage.